Sentence examples for imbalanced sampling from inspiring English sources

Sampling methods for FDP are classified and reviewed by the following paired groups, namely: training sampling and testing sampling, single industry sampling and cross-industry sampling, balanced sampling and imbalanced sampling.

We concur that the evidence for latitudinally arrayed dinosaur assemblages during the Maastrichtian is relatively weak, given the poor stratigraphic control and greatly imbalanced sampling.

These findings suggest that insufficient information on the spatial variability of soil respiration and imbalanced sampling could bias estimates of current and future carbon budgets.

Focused on regression modeling between those two types of process variables with imbalanced sampling values, this paper develops a semisupervised form of the Probabilistic Partial Least Squares (PPLS) model.

The sampsize parameter controls whether to use balanced or imbalanced sampling to generate the training datasets; mtry specifies the number of variables to be randomly selected and tested at each node of each tree; and ntree determines the number of trees to be grown in a forest.

To control for potential reporting bias due to imbalanced sampling efforts to collect and report data on flavivirus hosts, we included the number of primary publications in PubMed using a MeSH term search for the avian and mammalian species as a proxy for study efforts, assuming that surveillance efforts for vertebrate species will be proportional to the number of citations available for them.

Discussion focuses on the antiquated, fuzzy dichotomous descriptors of patrilocal/matrilocal residence rules, trait heritability, the utility of dental metric data, and small and imbalanced samples.

As a result, an improved neural classifier is proposed, in which balanced learning with optimized decision making are introduced to enable effective learning from imbalanced samples.

In order to avoid the difficulties arising when analyzing imbalanced samples, we use the Random OverSampling Examples (Rose) approach that has been recently proposed in the statistical literature to obtain representative balanced samples from imbalanced ones.

To deal with the imbalanced samples, we adopt cost-sensitive ensemble technique to build the prediction models.

Because of imbalanced lineage sampling, the data do not speak unambiguously in favor on any particular theory.